Histogram Segmentation Based Local Adaptive Filter for Video Encoding and Decoding

ABSTRACT

Reconstructed picture quality for a video codec system may be improved by categorizing reconstructed pixels into different histogram bins with histogram segmentation and then applying different filters on different bins. Histogram segmentation may be performed by averagely dividing the histogram into M bins or adaptively dividing the histogram into N bins based on the histogram characteristics. Here M and N may be a predefined, fixed, non-negative integer value or an adaptively generated value at encoder side and may be sent to decoder through the coded bitstream.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation claiming priority to U.S. patentapplication Ser. No. 13/991,592, filed Jun. 4, 2013, hereby expresslyincorporated by reference.

BACKGROUND

This relates to video compression.

In lossy video coding, quantization of transform coefficients willintroduce quality degradation to reconstructed pictures. A largerquantization step introduces bigger picture quality loss.

To improve the reconstructed picture quality and improve the videocompression gain, an adaptive filter may be applied as an out-loop videoprocessing tool or as part of in-loop modules in the core video codingpipeline to partially compensate for the picture quality loss.

BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments are described with respect to the following figures:

FIG. 1 shows one example of an encoding flow for a filtering decisionwhen multiple histogram segmentation methods are applied.

FIG. 2 shows the corresponding decoder flow.

FIG. 3 is a system depiction for one embodiment.

FIG. 4 is a front elevational view of one embodiment.

DETAILED DESCRIPTION

Having considered that a global Wiener filter may lose adaptation tosome local pixel information, one could apply a Wiener filter locally onthe adaptively selected pixels, use a histogram segmentation scheme tohelp select pixels for filtering. But a Wiener filter may lack theability to handle constant quality loss within a histogram bin.Therefore, a modified Wiener filter with offset may be used toadaptively filter histogram bins.

Reconstructed picture quality for a video codec system may be improvedby categorizing reconstructed pixels into different histogram bins withhistogram segmentation and then applying different filters on differentbins. Histogram segmentation may be performed by evenly dividing thehistogram into M bins or adaptively dividing the histogram into N binsbased on the histogram characteristics. For example, if the pixel valuerange is 0-255, then evenly dividing the histogram into M bins meansthat each bin has the interval of 256/M pixel values. Here M and N maybe a predefined, fixed, non-negative integer value or an adaptivelygenerated value at the encoder side and may be sent to the decoderthrough the coded bitstream. Histogram characteristics involvedistributions of pixel values. There are many published papersintroducing methods of histogram segmentation and/or partitioning.

Reconstructed picture quality may be improved for the video codec systemby applying multiple available histogram segmentation schemes anddetermining the best segmentation scheme for filtering based on the RateDistortion Optimization (RDO) criterion at the encoder side, and thensending a flag to the decoder to indicate the determination result.Reconstructed picture quality for the video codec system may be improvedby adaptively applying filters on the segmented histogram bins based onan RDO criterion, and then sending flags through the coded bitstream toindicate which histogram bins should be filtered.

An adaptive Wiener filter may be applied with offset for a histogrambin. The Wiener filter coefficients and the offset value for a histogrambin may be generated adaptively at the encoder side and then thecoefficients and the offset value are sent to the decoder through thecoded bitstream. The Weiner filter with offset is applied onreconstructed pixels P(x, y) in a histogram bin to get filtered pixelvalue P′(x, y) for the bin. Then the Weiner filter coefficients and theoffset value may be calculated by minimizing the sum of absolutedifferences (SSD) between the filtered pixel values P′_((x,y)) and theoriginal input pixel values Q(x, y), as shown in FIG. 1 at the encoderside and then the coefficients and the offset value are sent to decoderthrough the coded bitstream. In some embodiments, the offset value maybe forced to zero, and only the Wiener filter coefficients need to begenerated at the encoder side and then sent to the decoder.

In some embodiments, an offset value is only applied to a histogram binwithout applying the Wiener filter. In this case, only the offset valueneeds to be generated for a histogram bin at the encoder side and thensent to the decoder.

In some embodiments, some of the Wiener filter coefficients may beforced to be zero to save the transmission bandwidth, and then thesecoefficients need not be sent to decoder.

Reconstructed picture quality for the video codec system may be improvedby adaptively selecting global filtering or histogram-segmentation basedlocal filtering based on RDO criterion at encoder side, and then sendinga flag to the decoder to indicate the selection result. The globalfiltering is in fact a special case of histogram-segmentation basedlocal filtering. If the whole histogram is regarded as a single bin (nosegmentation), then the local filtering is in fact global filtering onall pixels in the picture. In FIG. 1, one histogram segmentation methodcan be set to “do not segment” for this case.

Reconstructed picture quality for the video codec system may be improvedby adaptively determining the frame to be filtered (or not) based on RDOcriteria at the encoder side, and then sending a flag to the decoder toindicate the determination result.

In a video codec, an adaptive Wiener filter aims to minimize thedifferences between two input pictures or picture regions, and thefilter coefficients that need to be transmitted to decoder side. LetQ(x,y) denote the value of the encoder input pixel at position (x,y),and P(x,y) denote the value of the reconstructed pre-filtering pixel atposition (x,y). Adaptive Wiener filtering with offset is performed onP(x,y) as indicated equation (1) to get the post-filter pixel valueP′(x,y):

P′(x,y)=ρ_(m=−M) ₀ ^(M) ¹ Σ_(n=−N) ₀ ^(N) ¹ P(x+m, y+n)C _(m,n)+Offset  (1)

Where, C_(m,n) denotes the adaptive filtering coefficients, and Offsetdenotes the offset value added to the corresponding histogram bins. M₀,M₁, N₀, N₁ are parameters to control the number of Wiener filter taps.With different settings in the following list of variables of M₀, M₁,N₀, N₁, the filter may be a symmetric filter or asymmetric filter, a 1-Dfilter or 2-D filter. The parameters M₀, M₁, N₀, N₁ may be set topredefined fixed values. These fixed values may be presented in videocoding standard specifications so that both the encoder and decoder canuse the same values. Alternatively, M₀, M₁, N₀, N₁ may have severalpredefined candidate values. Then the encoder can select the bestcandidate based on the RDO criteria, and then send a flag to the decoderto indicate which candidate to use.

The coefficients C_(m,n) and offset value Offset may be adaptivelygenerated at the encoder side and then may be coded into bitstreams fordecoding. One method to generate C_(m,n) and Offset values is tominimize the sum of squared distortions between Q(x,y) and P′(x,y).

The derivation of the filter taps is provided below. For easydescription, one can map the 2D filter tap Cmn to 1D filter tap Ci, andmap the 2D pixel index (x,y) to 1D pixel index (i). Consider the inputpixel Q(k) and the output of the Wiener filter P′(k) consisting of thereconstructed mapped pixel P(k) in the filter support {S}, sized as L+1,with the weight C_(i). The adaptive (Wiener) filter function is

$\begin{matrix}{{P^{\prime}(k)} = {\sum\limits_{i \in {\{ S\}}}{{P\left( {k + i} \right)} \cdot C_{i}}}} & \lbrack 2\rbrack\end{matrix}$

The residual signal among input pixel Q(i) and the Wiener filtered pixelP′(i) is defined as

error_(k) =P′(k)−Q(k)   [3]

The Wiener filter is optimal by minimizing the mean square error withthe filter taps {C_(i)}

C _(i)=arg min E[error_(k) ²]  [4]

where E[ ] is the expectation of the square of the residual signal forthe pixels of interest which could be the pixels from a sequence ofpictures, a picture, or some region inside a picture.

$\begin{matrix}{\left. {{E\left\lbrack {error}_{k}^{2} \right\rbrack} = {E\left\lbrack \left( {{P^{\prime}(k)} - {Q(k)}^{2}} \right. \right.}} \right\rbrack = {{E\left\lbrack \left( {\sum\limits_{i \in {\{ S\}}}{{P\left( {k + i} \right)} \cdot C_{i}}} \right)^{2} \right\rbrack} + {E\left\lbrack {\left( {Q(k)}^{2} \right\rbrack - {2{E\left\lbrack \left( {\sum\limits_{i \in {\{ S\}}}{\left( {{P\left( {k + i} \right)} \cdot C_{i}} \right)\left( {Q(k)} \right\rbrack}} \right. \right.}}} \right.}}} & \lbrack 5\rbrack\end{matrix}$

To find the minimum of E[error_(k) ²], the derivative is taken withrespect to C_(i). The filter taps could be derived by letting thederivative be equal to zero,

$\begin{matrix}{{\frac{\partial}{\partial c_{i}}{E\left\lbrack {error}_{k}^{2} \right\rbrack}} = {{{2\left( {\sum\limits_{j \in {\{ S\}}}{E\left\{ {{P\left( {k + j} \right)} \cdot {P\left( {k + i} \right)}} \right\} C_{i}}} \right)} - {2{E\left\lbrack {{P\left( {k + i} \right)} \cdot {Q(k)}} \right\rbrack}}} = 0}} & \lbrack 6\rbrack\end{matrix}$

The autocorrelation function of P(k) is denoted below Equation [7] andthe cross-correlation function among P(k) and Q(k) is denoted belowEquation [8].

r _(PP)(i)=E[P(k)·P(k+i)]  [7]

r _(QP)(i)=E[Q(k)·P(k+i)]  [8]

One can rewrite Equation [5] in the matrix form

$\begin{matrix}{{\begin{bmatrix}{r_{PP}(0)} & {r_{PP}(1)} & \cdots & {r_{PP}(L)} \\{r_{PP}(1)} & {r_{PP}(0)} & \cdots & {r_{PP}\left( {L - 1} \right)} \\\vdots & \vdots & \ddots & \vdots \\{r_{PP}(L)} & {r_{PP}\left( {L - 1} \right)} & \cdots & {r_{PP}(0)}\end{bmatrix}\begin{bmatrix}c_{0} \\c_{1} \\\vdots \\c_{L}\end{bmatrix}} = \begin{bmatrix}{r_{QP}(0)} \\{r_{QP}(1)} \\\vdots \\{r_{QP}(L)}\end{bmatrix}} & \lbrack 9\rbrack\end{matrix}$

Thus, the Wiener filter tap set {C} can be derived in the matrix format

R _(PP) ·C=R _(QP) →C=R _(PP) ⁻¹R_(QP)   [10]

where R_(PP) ⁻¹ is the inverse matrix of the auto-correlation matrix inEquation [9]. After obtaining Ci, it could be mapped back to Cmn. Thenthe offset could be calculated by

${{Offset} = {\frac{1}{N}{\sum\limits_{x,y}\left( {{P^{\prime}\left( {x,y} \right)} - {Q\left( {x,y} \right)}} \right)}}},$

where N is the number of pixels in the histogram bin.

The collection of auto-correlation functions in Equations [7] and [10]can be obtained at the video decoder side, but the cross-correlation inEquations [8] and [10] has to be derived at the video encoder side dueto the requirement of the input {x} only being available at videoencoder side. Thus, we need to transmit the filter taps derived inEquation [10] from video encoder to video decoder.

The transmitting of the cross-correlation function, instead of thederived filter taps, is sufficient in some cases because the videodecoder could derive the filter taps with the reception ofcross-correlation function plus the decoded deblocked data {y} at itsown hand.

More accurate statistical information may be obtained in some cases toimprove coding efficiency further by skipping the pixels close to thepicture border. The right hand side of Equation [10] is an expression ofthis skipping.

The filter taps could be derived per luma and per chroma channelrespectively. Better coding efficiency is achieved for the chromapicture based on the filter taps derived with only chroma pixel. Somescenarios use the one chroma table shared by both Cb and Cr channels, orone can use two individual tables for Cb and Cr respectively.

This approach is scalable and it could be extended to include thedeblocked pictures, in addition to the adaptive filtered picture, toserve as the reference picture for the phase of the motion estimation.This doubles the amount of the reference pictures to improve theaccuracy of motion estimation, without extra information being sent fromthe video encoder side, because the deblocked picture is alwaysaccessible on the video decoder side.

FIG. 4 shows a delay module is placed after deblocking filter, prior tothe adaptive filter, on FIG. 2. With the module of the delay, theproduction of the adaptive filter taps can be re-calculated per eachpicture time based on the current input picture versus the referencepictures in the buffer list. Thus, the video encoder updates the filtertaps for each reference picture. Similarly FIG. 5 depicts there-position of the delay module in FIG. 3.

FIG. 6 shows the modules of statistical feature collector and theadaptive filter added to the output of the motion compensated picture tofind the solution of minimal mean square error among the input video andthe motion compensated picture. This leads to better coding efficiencyin some cases. This adaptive filter after the module of the motioncompensation is independent of the adaptive filter before the module ofmotion estimation as depicted in FIGS. 2-5. Thus, this could be alsoserve as the add-on on top of FIGS. 2-5.

In some scenarios, the coding efficiency of the adaptive filter isbetter than the case of only applying the deblocking filter. That is,one can apply the adaptive filter, but remove the deblocking filter fromthe core coding loop.

When filtering a picture with a global Wiener filter, the filtercoefficients are trained with all the pixels in the picture. In thiscase, the filtering will reduce the distortions of some pixels, but itwill also increase the distortions of other pixels. So, more coding gainmay be obtained by only perform the Wiener filtering on part of thepixels in the picture.

One method is to categorize pixels into groups with histogramsegmentation and then perform Wiener filtering adaptively (depending onRDO criterion) on each histogram bin. Histogram segmentation dividespixels values into multiple bins, wherein one bin is a group of pixelswhose values fall into the bin. Suppose that the histogram is dividedinto N bins (with any kind of histogram segmentation method includinguniform or non-uniform methods). Then, the encoder can decide which binsto filter and generate filtering parameters, i.e., coefficients C_(m,n)and Offset, for those to-be filtered bins, and then transmit the relatedinformation to the decoder.

When applying multiple histogram segmentation methods, the encoder candetermine which RDO criteria is used to filter the current picture andthen send the determination result to decoder. The encoder can do multitask encoding (one pass for one segmentation method) and calculate therate distortion (RD) cost for each pass. Then the pass with the minimumRD cost will be used for the final encoding.

Referring to FIG. 1, the value of the reconstructed pre-filtering pixelat position (x,y), P(x,y), may be fed to a histogram segmentation method12 a-k. In this case multiple histogram segmentation methods are appliedand each one operates on the value. This produces outputs that areprovided to adaptive filter decisions 14 a-14 k. Also applied to theadaptive filter decision 14 a-k is the value of the encoder input pixelat position (x,y), Q (x,y). Based on these inputs, the adaptive filterdecisions output a value to the histogram segmentation method decision16 which then produces the desired result. The adaptive filter decisionincludes the adaptive Wiener filter with offset.

Referring to FIG. 2, which relates to the decoder as opposed to theencoder side, the value P(x,y) is applied to the histogram segmentationwith received method block 18. When multiple histograms segmentationmethods are defined, the decoder will receive a flag from the encoderindicating which segmentation method was selected by the encoder. Afterhaving received the method flag, the decoder can do histogramsegmentation on P(x,y) with the received method. Its output is providedto an adaptive filter on histogram bins with received filter parameters20 to output the post-filter pixel value P′(x,y).

FIG. 3 illustrates an embodiment of a system 700. In embodiments, system700 may be a media system although system 700 is not limited to thiscontext. For example, system 700 may be incorporated into a personalcomputer (PC), laptop computer, ultra-laptop computer, tablet, touchpad, portable computer, handheld computer, palmtop computer, personaldigital assistant (PDA), cellular telephone, combination cellulartelephone/PDA, television, smart device (e.g., smart phone, smart tabletor smart television), mobile internet device (MID), messaging device,data communication device, and so forth.

In embodiments, system 700 comprises a platform 702 coupled to a display720. Platform 702 may receive content from a content device such ascontent services device(s) 730 or content delivery device(s) 740 orother similar content sources. A navigation controller 750 comprisingone or more navigation features may be used to interact with, forexample, platform 702 and/or display 720. Each of these components isdescribed in more detail below.

In embodiments, platform 702 may comprise any combination of a chipset705, processor 710, memory 712, storage 714, graphics subsystem 715,applications 716, global positioning system (GPS) 721, camera 723 and/orradio 718. Chipset 705 may provide intercommunication among processor710, memory 712, storage 714, graphics subsystem 715, applications 716and/or radio 718. For example, chipset 705 may include a storage adapter(not depicted) capable of providing intercommunication with storage 714.

In addition, the platform 702 may include an operating system 770. Aninterface to the processor 772 may interface the operating system andthe processor 710.

Firmware 790 may be provided to implement functions such as the bootsequence. An update module to enable the firmware to be updated fromoutside the platform 702 may be provided. For example the update modulemay include code to determine whether the attempt to update is authenticand to identify the latest update of the firmware 790 to facilitate thedetermination of when updates are needed.

In some embodiments, the platform 702 may be powered by an externalpower supply. In some cases, the platform 702 may also include aninternal battery 780 which acts as a power source in embodiments that donot adapt to external power supply or in embodiments that allow eitherbattery sourced power or external sourced power.

The sequences shown in FIGS. 1 and 2 may be implemented in software andfirmware embodiments by incorporating them within the storage 714 orwithin memory within the processor 710 or the graphics subsystem 715 tomention a few examples. The graphics subsystem 715 may include thegraphics processing unit and the processor 710 may be a centralprocessing unit in one embodiment.

Processor 710 may be implemented as Complex Instruction Set Computer(CISC) or Reduced Instruction Set Computer (RISC) processors, x86instruction set compatible processors, multi-core, or any othermicroprocessor or central processing unit (CPU). In embodiments,processor 710 may comprise dual-core processor(s), dual-core mobileprocessor(s), and so forth.

Memory 712 may be implemented as a volatile memory device such as, butnot limited to, a Random Access Memory (RAM), Dynamic Random AccessMemory (DRAM), or Static RAM (SRAM).

Storage 714 may be implemented as a non-volatile storage device such as,but not limited to, a magnetic disk drive, optical disk drive, tapedrive, an internal storage device, an attached storage device, flashmemory, battery backed-up SDRAM (synchronous DRAM), and/or a networkaccessible storage device. In embodiments, storage 714 may comprisetechnology to increase the storage performance enhanced protection forvaluable digital media when multiple hard drives are included, forexample.

Graphics subsystem 715 may perform processing of images such as still orvideo for display. Graphics subsystem 715 may be a graphics processingunit (GPU) or a visual processing unit (VPU), for example. An analog ordigital interface may be used to communicatively couple graphicssubsystem 715 and display 720. For example, the interface may be any ofa High-Definition Multimedia Interface, DisplayPort, wireless HDMI,and/or wireless HD compliant techniques. Graphics subsystem 715 could beintegrated into processor 710 or chipset 705. Graphics subsystem 715could be a stand-alone card communicatively coupled to chipset 705.

The graphics and/or video processing techniques described herein may beimplemented in various hardware architectures. For example, graphicsand/or video functionality may be integrated within a chipset.Alternatively, a discrete graphics and/or video processor may be used.As still another embodiment, the graphics and/or video functions may beimplemented by a general purpose processor, including a multi-coreprocessor. In a further embodiment, the functions may be implemented ina consumer electronics device.

Radio 718 may include one or more radios capable of transmitting andreceiving signals using various suitable wireless communicationstechniques. Such techniques may involve communications across one ormore wireless networks. Exemplary wireless networks include (but are notlimited to) wireless local area networks (WLANs), wireless personal areanetworks (WPANs), wireless metropolitan area network (WMANs), cellularnetworks, and satellite networks. In communicating across such networks,radio 718 may operate in accordance with one or more applicablestandards in any version.

In embodiments, display 720 may comprise any television type monitor ordisplay. Display 720 may comprise, for example, a computer displayscreen, touch screen display, video monitor, television-like device,and/or a television. Display 720 may be digital and/or analog. Inembodiments, display 720 may be a holographic display. Also, display 720may be a transparent surface that may receive a visual projection. Suchprojections may convey various forms of information, images, and/orobjects. For example, such projections may be a visual overlay for amobile augmented reality (MAR) application. Under the control of one ormore software applications 716, platform 702 may display user interface722 on display 720.

In embodiments, content services device(s) 730 may be hosted by anynational, international and/or independent service and thus accessibleto platform 702 via the Internet, for example. Content servicesdevice(s) 730 may be coupled to platform 702 and/or to display 720.Platform 702 and/or content services device(s) 730 may be coupled to anetwork 760 to communicate (e.g., send and/or receive) media informationto and from network 760. Content delivery device(s) 740 also may becoupled to platform 702 and/or to display 720.

In embodiments, content services device(s) 730 may comprise a cabletelevision box, personal computer, network, telephone, Internet enableddevices or appliance capable of delivering digital information and/orcontent, and any other similar device capable of unidirectionally orbidirectionally communicating content between content providers andplatform 702 and/display 720, via network 760 or directly. It will beappreciated that the content may be communicated unidirectionally and/orbidirectionally to and from any one of the components in system 700 anda content provider via network 760. Examples of content may include anymedia information including, for example, video, music, medical andgaming information, and so forth.

Content services device(s) 730 receives content such as cable televisionprogramming including media information, digital information, and/orother content. Examples of content providers may include any cable orsatellite television or radio or Internet content providers. Theprovided examples are not meant to limit embodiments of the invention.

In embodiments, platform 702 may receive control signals from navigationcontroller 750 having one or more navigation features. The navigationfeatures of controller 750 may be used to interact with user interface722, for example. In embodiments, navigation controller 750 may be apointing device that may be a computer hardware component (specificallyhuman interface device) that allows a user to input spatial (e.g.,continuous and multi-dimensional) data into a computer. Many systemssuch as graphical user interfaces (GUI), and televisions and monitorsallow the user to control and provide data to the computer or televisionusing physical gestures.

Movements of the navigation features of controller 750 may be echoed ona display (e.g., display 720) by movements of a pointer, cursor, focusring, or other visual indicators displayed on the display. For example,under the control of software applications 716, the navigation featureslocated on navigation controller 750 may be mapped to virtual navigationfeatures displayed on user interface 722, for example. In embodiments,controller 750 may not be a separate component but integrated intoplatform 702 and/or display 720. Embodiments, however, are not limitedto the elements or in the context shown or described herein.

In embodiments, drivers (not shown) may comprise technology to enableusers to instantly turn on and off platform 702 like a television withthe touch of a button after initial boot-up, when enabled, for example.Program logic may allow platform 702 to stream content to media adaptorsor other content services device(s) 730 or content delivery device(s)740 when the platform is turned “off.” In addition, chip set 705 maycomprise hardware and/or software support for 5.1 surround sound audioand/or high definition 7.1 surround sound audio, for example. Driversmay include a graphics driver for integrated graphics platforms. Inembodiments, the graphics driver may comprise a peripheral componentinterconnect (PCI) Express graphics card.

In various embodiments, any one or more of the components shown insystem 700 may be integrated. For example, platform 702 and contentservices device(s) 730 may be integrated, or platform 702 and contentdelivery device(s) 740 may be integrated, or platform 702, contentservices device(s) 730, and content delivery device(s) 740 may beintegrated, for example. In various embodiments, platform 702 anddisplay 720 may be an integrated unit. Display 720 and content servicedevice(s) 730 may be integrated, or display 720 and content deliverydevice(s) 740 may be integrated, for example. These examples are notmeant to limit the invention.

In various embodiments, system 700 may be implemented as a wirelesssystem, a wired system, or a combination of both. When implemented as awireless system, system 700 may include components and interfacessuitable for communicating over a wireless shared media, such as one ormore antennas, transmitters, receivers, transceivers, amplifiers,filters, control logic, and so forth. An example of wireless sharedmedia may include portions of a wireless spectrum, such as the RFspectrum and so forth. When implemented as a wired system, system 700may include components and interfaces suitable for communicating overwired communications media, such as input/output (I/O) adapters,physical connectors to connect the I/O adapter with a correspondingwired communications medium, a network interface card (NIC), disccontroller, video controller, audio controller, and so forth. Examplesof wired communications media may include a wire, cable, metal leads,printed circuit board (PCB), backplane, switch fabric, semiconductormaterial, twisted-pair wire, co-axial cable, fiber optics, and so forth.

Platform 702 may establish one or more logical or physical channels tocommunicate information. The information may include media informationand control information. Media information may refer to any datarepresenting content meant for a user. Examples of content may include,for example, data from a voice conversation, videoconference, streamingvideo, electronic mail (“email”) message, voice mail message,alphanumeric symbols, graphics, image, video, text and so forth. Datafrom a voice conversation may be, for example, speech information,silence periods, background noise, comfort noise, tones and so forth.Control information may refer to any data representing commands,instructions or control words meant for an automated system. Forexample, control information may be used to route media informationthrough a system, or instruct a node to process the media information ina predetermined manner. The embodiments, however, are not limited to theelements or in the context shown or described in FIG. 3.

As described above, system 700 may be embodied in varying physicalstyles or form factors. FIG. 4 illustrates embodiments of a small formfactor device 800 in which system 700 may be embodied. In embodiments,for example, device 800 may be implemented as a mobile computing devicehaving wireless capabilities. A mobile computing device may refer to anydevice having a processing system and a mobile power source or supply,such as one or more batteries, for example.

As described above, examples of a mobile computing device may include apersonal computer (PC), laptop computer, ultra-laptop computer, tablet,touch pad, portable computer, handheld computer, palmtop computer,personal digital assistant (PDA), cellular telephone, combinationcellular telephone/PDA, television, smart device (e.g., smart phone,smart tablet or smart television), mobile internet device (MID),messaging device, data communication device, and so forth.

Examples of a mobile computing device also may include computers thatare arranged to be worn by a person, such as a wrist computer, fingercomputer, ring computer, eyeglass computer, belt-clip computer, arm-bandcomputer, shoe computers, clothing computers, and other wearablecomputers. In embodiments, for example, a mobile computing device may beimplemented as a smart phone capable of executing computer applications,as well as voice communications and/or data communications. Althoughsome embodiments may be described with a mobile computing deviceimplemented as a smart phone by way of example, it may be appreciatedthat other embodiments may be implemented using other wireless mobilecomputing devices as well. The embodiments are not limited in thiscontext.

As shown in FIG. 4, device 800 may comprise a housing 802, a display804, an input/output (I/O) device 806, and an antenna 808. Device 800also may comprise navigation features 812. Display 804 may comprise anysuitable display unit for displaying information appropriate for amobile computing device. I/O device 806 may comprise any suitable I/Odevice for entering information into a mobile computing device. Examplesfor I/O device 806 may include an alphanumeric keyboard, a numerickeypad, a touch pad, input keys, buttons, switches, rocker switches,microphones, speakers, voice recognition device and software, and soforth. Information also may be entered into device 800 by way ofmicrophone. Such information may be digitized by a voice recognitiondevice. The embodiments are not limited in this context.

Various embodiments may be implemented using hardware elements, softwareelements, or a combination of both. Examples of hardware elements mayinclude processors, microprocessors, circuits, circuit elements (e.g.,transistors, resistors, capacitors, inductors, and so forth), integratedcircuits, application specific integrated circuits (ASIC), programmablelogic devices (PLD), digital signal processors (DSP), field programmablegate array (FPGA), logic gates, registers, semiconductor device, chips,microchips, chip sets, and so forth. Examples of software may includesoftware components, programs, applications, computer programs,application programs, system programs, machine programs, operatingsystem software, middleware, firmware, software modules, routines,subroutines, functions, methods, procedures, software interfaces,application program interfaces (API), instruction sets, computing code,computer code, code segments, computer code segments, words, values,symbols, or any combination thereof. Determining whether an embodimentis implemented using hardware elements and/or software elements may varyin accordance with any number of factors, such as desired computationalrate, power levels, heat tolerances, processing cycle budget, input datarates, output data rates, memory resources, data bus speeds and otherdesign or performance constraints.

One or more aspects of at least one embodiment may be implemented byrepresentative instructions stored on a machine-readable medium whichrepresents various logic within the processor, which when read by amachine causes the machine to fabricate logic to perform the techniquesdescribed herein. Such representations, known as “IP cores” may bestored on a tangible, machine readable medium and supplied to variouscustomers or manufacturing facilities to load into the fabricationmachines that actually make the logic or processor.

Various embodiments may be implemented using hardware elements, softwareelements, or a combination of both. Examples of hardware elements mayinclude processors, microprocessors, circuits, circuit elements (e.g.,transistors, resistors, capacitors, inductors, and so forth), integratedcircuits, application specific integrated circuits (ASIC), programmablelogic devices (PLD), digital signal processors (DSP), field programmablegate array (FPGA), logic gates, registers, semiconductor device, chips,microchips, chip sets, and so forth. Examples of software may includesoftware components, programs, applications, computer programs,application programs, system programs, machine programs, operatingsystem software, middleware, firmware, software modules, routines,subroutines, functions, methods, procedures, software interfaces,application program interfaces (API), instruction sets, computing code,computer code, code segments, computer code segments, words, values,symbols, or any combination thereof. Determining whether an embodimentis implemented using hardware elements and/or software elements may varyin accordance with any number of factors, such as desired computationalrate, power levels, heat tolerances, processing cycle budget, input datarates, output data rates, memory resources, data bus speeds and otherdesign or performance constraints.

One or more aspects of at least one embodiment may be implemented byrepresentative instructions stored on a machine-readable medium whichrepresents various logic within the processor, which when read by amachine causes the machine to fabricate logic to perform the techniquesdescribed herein. Such representations, known as “IP cores” may bestored on a tangible, machine readable medium and supplied to variouscustomers or manufacturing facilities to load into the fabricationmachines that actually make the logic or processor.

The graphics processing techniques described herein may be implementedin various hardware architectures. For example, graphics functionalitymay be integrated within a chipset. Alternatively, a discrete graphicsprocessor may be used. As still another embodiment, the graphicsfunctions may be implemented by a general purpose processor, including amulticore processor.

The following clauses and/or examples pertain to further embodiments:

One example embodiment may be a method adaptively filtering histogrambins for video encoding using a Wiener filter with offset. Anotherexample embodiment may be using multiple bin segmentation methods. Themethod may also include evenly dividing the histogram into bins. Themethod may also include adaptively dividing the histogram into bins. Themethod may include sending the number of bins from the encoder to adecoder. The method may include identifying a segmentation method basedon Rate Distortion Optimization criteria. The method may also includegenerating adaptive filter coefficients by minimizing a sum of squareddistortions between the value of an input pixel at a position and thepost filter pixel value at that position.

Another example embodiment may be at least one or more computer readablemedia comprising storing instructions executed by a computer to use aWeiner filter with offset to adaptively filter histogram bins for videoencoding. One example embodiment may be further storing instructions touse multiple bin segmentation methods. The media may further storeinstructions to divide the histogram into bins. The media may furtherstore instructions to adaptively divide the histogram into bins. Themedia may further store instructions to send the number of bins from theencoder to a decoder. The media may further store instructions toidentify a segmentation method based on Rate Distortion Optimizationcriteria. The media may further store instructions to generate adaptivefilter coefficients by minimizing a sum of squared distortions betweenthe value of an input pixel at a position and the post filter pixelvalue at that position

Another example embodiment may be an apparatus comprising a Weinerfilter with offset; and a processor coupled to said filter to adaptivelyfilter histogram bins using said filter. The apparatus may use multiplebin segmentation. The apparatus may evenly divide the histogram intobins. The apparatus may adaptively divide the histogram into bins. Theapparatus may include an encoder and a decoder coupled to said encoder,said processor to send the number of bins from the encoder to thedecoder. The apparatus may also include a processor to identify asegmentation method based on rate distortion optimization criteria. Theapparatus may include said processor to generate adaptive filtercoefficients by minimizing a sum of square distortions between the valueof an input pixel at a position and the post pixel value at thatposition. The apparatus may also include an operating system, a batteryand firmware and a module to update said firmware.

The graphics processing techniques described herein may be implementedin various hardware architectures. For example, graphics functionalitymay be integrated within a chipset. Alternatively, a discrete graphicsprocessor may be used. As still another embodiment, the graphicsfunctions may be implemented by a general purpose processor, including amulticore processor.

References throughout this specification to “one embodiment” or “anembodiment” mean that a particular feature, structure, or characteristicdescribed in connection with the embodiment is included in at least oneimplementation encompassed within the present invention. Thus,appearances of the phrase “one embodiment” or “in an embodiment” are notnecessarily referring to the same embodiment. Furthermore, theparticular features, structures, or characteristics may be instituted inother suitable forms other than the particular embodiment illustratedand all such forms may be encompassed within the claims of the presentapplication.

While the present invention has been described with respect to a limitednumber of embodiments, those skilled in the art will appreciate numerousmodifications and variations therefrom. It is intended that the appendedclaims cover all such modifications and variations as fall within thetrue spirit and scope of this present invention.

What is claimed is:
 1. A method comprising: using multiple bin segmentation methods; separately adaptively filtering histogram bins for each of said segmentation methods for video encoding using a Wiener filter with offset adapted to each of said bins; and setting the offset based on filtered values output from Wiener filter and original input pixel values input to the Wiener filter and the number of pixels in a histogram bin.
 2. The method of claim 1 including evenly dividing the histogram into bins.
 3. The method of claim 1 including adaptively dividing the histogram into bins.
 4. The method of claim 1 including sending the number of bins from the encoder to a decoder.
 5. The method of claim 1 including identifying a segmentation method based on Rate Distortion Optimization criteria.
 6. The method of claim 1 including generating adaptive filter coefficients by minimizing a sum of squared distortions between the value of an input pixel at a position and the post filter pixel value at that position.
 7. One or more non-transitory computer readable media storing instructions executed by a computer to use a Weiner filter with offset to separately adaptively filter histogram bins for multiple bin segmentation methods for video encoding said filter offset adapted to each of said bins, and to set the offset based on filtered values output from Wiener filter and original input pixel values input to the Wiener filter and the number of pixels in a histogram bin.
 8. The media of claim 7 further storing instructions to use multiple bin segmentation methods.
 9. The media of claim 8 further storing instructions to divide the histogram into bins.
 10. The media of claim 8 further storing instructions to adaptively dividing the histogram into bins.
 11. The media of claim 7 further storing instructions to send the number of bins from the encoder to a decoder.
 12. The media of claim 8 further storing instructions to identify a segmentation method based on Rate Distortion Optimization criteria.
 13. The media of claim 7 further storing instructions to generate adaptive filter coefficients by minimizing a sum of squared distortions between the value of an input pixel at a position and the post filter pixel value at that position.
 14. An apparatus comprising: a Weiner filter with offset; and a processor coupled to said filter to separately adaptively filter histogram bins for each of said segmentation methods using said filter, the filter offset adapted to each of said bins, and to set the offset based on filtered values output from Wiener filter and original input pixel values input to the Wiener filter and the number of pixels in a histogram bin.
 15. The apparatus of claim 14 said processor to use multiple bin segmentation.
 16. The apparatus of claim 14 said processor to evenly divide the histogram into bins.
 17. The apparatus of claim 15 said processor to adaptively divide the histogram into bins.
 18. The apparatus of claim 14 including an encoder and a decoder coupled to said encoder, said processor to send the number of bins from the encoder to the decoder.
 19. The apparatus of claim 15, said processor to identify a segmentation method based on rate distortion optimization criteria.
 20. The apparatus of claim 14 said processor to generate adaptive filter coefficients by minimizing a sum of square distortions between the value of an input pixel at a position and the post pixel value at that position.
 21. The apparatus of claim 14 including an operating system.
 22. The apparatus of claim 14 including a battery.
 23. The apparatus of claim 14 including firmware and a module to update said firmware. 